sigmoidal

简明释义

英[sɪɡˈmɔɪdəl]美[sɪɡˈmɔɪdəl]

adj. S 形的; C 形的；反曲的（等于 sigmoid）

英英释义

单词用法

同义词

反义词

例句

1.The BP algorithm with the variable slope for the sigmoidal activation function accelerates the ANN training dramatically.

应用可变斜率的改进的BP算法，极大地加速神经网络的学习速度。

2.The BP algorithm with the variable slope for the sigmoidal activation function accelerates the ANN training dramatically.

应用可变斜率的改进的BP算法，极大地加速神经网络的学习速度。

3.In machine learning, the activation function often used in neural networks is the sigmoidal S型 function.

在机器学习中，神经网络中常用的激活函数是sigmoidal S型 函数。

4.The sigmoidal S型 response curve is useful for modeling dose-response relationships in pharmacology.

在药理学中，sigmoidal S型 响应曲线对于建模剂量-反应关系非常有用。

5.The enzyme activity showed a sigmoidal S型 relationship with substrate concentration.

酶活性与底物浓度呈现出sigmoidal S型 的关系。

6.A sigmoidal S型 trajectory can be observed in the adoption of new technologies over time.

在新技术的采用过程中，可以观察到一个sigmoidal S型 的轨迹。

7.The growth curve of the bacteria followed a sigmoidal S型 pattern, indicating phases of lag, exponential growth, and saturation.

细菌的生长曲线呈现出一个sigmoidal S型 的模式，表明了滞后期、指数增长期和饱和期。

作文

In the field of mathematics and biology, the term sigmoidal refers to a specific type of curve that resembles the letter 'S'. This characteristic shape is often observed in various natural phenomena, particularly in growth processes. The sigmoidal curve is crucial for understanding how certain populations grow over time, as it illustrates the relationship between the population size and resource availability. Initially, a population may grow slowly due to limited resources. As resources become more plentiful, the growth rate increases, leading to a rapid expansion phase. Eventually, as resources become scarce again, the growth rate slows down, resulting in a leveling off of the population size. This pattern can be seen in many species, including bacteria, plants, and animals. The sigmoidal function is also significant in the context of logistic regression, a statistical method used for predicting binary outcomes. In logistic regression, the dependent variable is modeled using a sigmoidal function, which allows for the prediction of probabilities that are constrained between 0 and 1. This constraint is particularly useful in fields such as medicine, where researchers may want to predict the likelihood of a patient developing a particular condition based on various risk factors. By utilizing the sigmoidal model, researchers can create more accurate predictions that help inform treatment decisions.Moreover, the concept of sigmoidal growth is not limited to biological systems. It can also be applied in economics, where the adoption of new technologies or products often follows a sigmoidal pattern. Initially, only a small number of early adopters embrace the innovation. As the benefits become more apparent, the adoption rate accelerates, eventually reaching a saturation point where most potential users have adopted the innovation. This understanding of sigmoidal growth patterns can aid businesses in strategizing their marketing efforts and anticipating market behavior.In conclusion, the term sigmoidal encapsulates a vital concept across various disciplines. Whether in biology, statistics, or economics, recognizing and understanding sigmoidal patterns allow researchers and practitioners to make informed decisions and predictions. The beauty of the sigmoidal curve lies in its ability to represent complex relationships in a simple yet profound manner. By studying these curves, we can gain insights into the dynamics of growth, adoption, and change in our world, ultimately contributing to our understanding of both natural and human-made systems.

在数学和生物学领域，术语sigmoidal指的是一种特定类型的曲线，其形状类似于字母“S”。这种特征形状常常出现在各种自然现象中，尤其是在生长过程中。sigmoidal曲线对于理解某些种群随时间增长的方式至关重要，因为它展示了种群规模与资源可用性之间的关系。最初，由于资源有限，种群可能会缓慢增长。随着资源变得更加丰富，增长率增加，导致快速扩展阶段。最终，随着资源再次变得稀缺，增长率减缓，导致种群规模趋于平稳。这种模式可以在许多物种中观察到，包括细菌、植物和动物。sigmoidal函数在逻辑回归的背景下也具有重要意义，逻辑回归是一种用于预测二元结果的统计方法。在逻辑回归中，因变量使用sigmoidal函数建模，这使得预测被限制在0和1之间。这种约束在医学等领域尤其有用，在这些领域中，研究人员可能希望根据各种风险因素预测患者发展某种特定病症的可能性。通过利用sigmoidal模型，研究人员可以创建更准确的预测，从而帮助指导治疗决策。此外，sigmoidal增长的概念并不仅限于生物系统。它也可以应用于经济学中，新技术或产品的采纳通常遵循sigmoidal模式。最初，只有少数早期采用者接受这一创新。随着好处变得更加明显，采纳率加速，最终达到一个饱和点，大多数潜在用户都已经采纳了这一创新。对sigmoidal增长模式的理解可以帮助企业制定市场营销策略并预测市场行为。总之，术语sigmoidal概括了各个学科中的一个重要概念。无论是在生物学、统计学还是经济学中，识别和理解sigmoidal模式使研究人员和从业者能够做出明智的决策和预测。sigmoidal曲线的美在于它能够以简单而深刻的方式代表复杂的关系。通过研究这些曲线，我们可以深入了解我们世界中增长、采纳和变化的动态，最终促进我们对自然和人造系统的理解。